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‎packages/sample-app/src/sample_langchain_bedrock.ts‎

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import { ChatBedrockConverse } from "@langchain/aws";
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import * as traceloop from "@traceloop/node-server-sdk";
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traceloop.initialize({
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appName: "sample_langchain_bedrock",
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apiKey: process.env.TRACELOOP_API_KEY,
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disableBatch: true,
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});
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async function main() {
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const model = new ChatBedrockConverse({
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model: "anthropic.claude-3-haiku-20240307-v1:0",
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});
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const response = await model.invoke("Tell me a joke about opentelemetry");
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console.log(response);
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}
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void main().then(() => {
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console.log("Done");
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});

‎packages/sample-app/src/sample_otel_sdk.ts‎

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import { NodeSDK } from "@opentelemetry/sdk-node";
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import { Resource } from "@opentelemetry/resources";
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import { SemanticResourceAttributes } from "@opentelemetry/semantic-conventions";
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import { createSpanProcessor } from "@traceloop/node-server-sdk";
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import { trace } from "@opentelemetry/api";
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// Initialize the OpenTelemetry SDK with Traceloop's span processor
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const sdk = new NodeSDK({
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resource: new Resource({
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[SemanticResourceAttributes.SERVICE_NAME]: "my-sample-app",
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}),
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spanProcessors: [
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createSpanProcessor({
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apiKey: process.env.TRACELOOP_API_KEY,
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baseUrl: process.env.TRACELOOP_BASE_URL,
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// Optional: disable batching for development
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disableBatch: process.env.NODE_ENV === "development",
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}),
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],
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});
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// Start the SDK
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sdk.start();
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// Your application code here
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async function main() {
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// Example: Create a trace
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const tracer = trace.getTracer("my-sample-app");
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const span = tracer.startSpan("main");
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try {
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// Simulate some work
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await new Promise(resolve => setTimeout(resolve, 1000));
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// Add some attributes that Traceloop's span processor will handle
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span.setAttribute("ai.prompt.messages", JSON.stringify([
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{ role: "user", content: "Hello, AI!" }
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]));
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span.end();
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} catch (error) {
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span.recordException(error);
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span.end();
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}
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}
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// Run the app
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main().then(() => {
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// Gracefully shut down the SDK
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sdk.shutdown()
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.then(() => console.log("Tracing terminated"))
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.catch((error) => console.log("Error terminating tracing", error))
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.finally(() => process.exit(0));
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}).catch((error) => {
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console.error(error);
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process.exit(1);
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});

‎packages/traceloop-sdk/src/lib/tracing/span-processor.ts‎

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import {
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SimpleSpanProcessor,
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BatchSpanProcessor,
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SpanProcessor,
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ReadableSpan,
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} from "@opentelemetry/sdk-trace-node";
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import { baggageUtils } from "@opentelemetry/core";
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import { Span, context } from "@opentelemetry/api";
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import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto";
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import { SpanExporter } from "@opentelemetry/sdk-trace-base";
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import {
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ASSOCATION_PROPERTIES_KEY,
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ENTITY_NAME_KEY,
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WORKFLOW_NAME_KEY,
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} from "./tracing";
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import { SpanAttributes } from "@traceloop/ai-semantic-conventions";
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export interface SpanProcessorOptions {
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/**
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* The API Key for sending traces data. Optional.
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* Defaults to the TRACELOOP_API_KEY environment variable.
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*/
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apiKey?: string;
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/**
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* The OTLP endpoint for sending traces data. Optional.
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* Defaults to TRACELOOP_BASE_URL environment variable or https://api.traceloop.com/
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*/
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baseUrl?: string;
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/**
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* Sends traces and spans without batching, for local development. Optional.
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* Defaults to false.
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*/
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disableBatch?: boolean;
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/**
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* The OpenTelemetry SpanExporter to be used for sending traces data. Optional.
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* Defaults to the OTLP exporter.
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*/
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exporter?: SpanExporter;
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/**
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* The headers to be sent with the traces data. Optional.
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*/
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headers?: Record<string, string>;
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}
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/**
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* Creates a span processor with Traceloop's custom span handling logic.
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* This can be used independently of the full SDK initialization.
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*
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* @param options - Configuration options for the span processor
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* @returns A configured SpanProcessor instance
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*/
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export const createSpanProcessor = (options: SpanProcessorOptions): SpanProcessor => {
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const headers =
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options.headers ||
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(process.env.TRACELOOP_HEADERS
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? baggageUtils.parseKeyPairsIntoRecord(process.env.TRACELOOP_HEADERS)
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: { Authorization: `Bearer ${options.apiKey}` });
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const traceExporter =
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options.exporter ??
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new OTLPTraceExporter({
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url: `${options.baseUrl || process.env.TRACELOOP_BASE_URL || "https://api.traceloop.com"}/v1/traces`,
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headers,
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});
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const spanProcessor = options.disableBatch
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? new SimpleSpanProcessor(traceExporter)
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: new BatchSpanProcessor(traceExporter);
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spanProcessor.onStart = (span: Span) => {
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const workflowName = context.active().getValue(WORKFLOW_NAME_KEY);
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if (workflowName) {
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span.setAttribute(
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SpanAttributes.TRACELOOP_WORKFLOW_NAME,
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workflowName as string,
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);
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}
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const entityName = context.active().getValue(ENTITY_NAME_KEY);
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if (entityName) {
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span.setAttribute(
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SpanAttributes.TRACELOOP_ENTITY_PATH,
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entityName as string,
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);
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}
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const associationProperties = context
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.active()
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.getValue(ASSOCATION_PROPERTIES_KEY);
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if (associationProperties) {
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for (const [key, value] of Object.entries(associationProperties)) {
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span.setAttribute(
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`${SpanAttributes.TRACELOOP_ASSOCIATION_PROPERTIES}.${key}`,
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value,
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);
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}
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}
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};
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spanProcessor.onEnd = (span: ReadableSpan) => {
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// Vercel AI Adapters
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const attributes = span.attributes;
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// Adapt span names
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const nameMap: Record<string, string> = {
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"ai.generateText.doGenerate": "ai.generateText.generate",
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"ai.streamText.doStream": "ai.streamText.stream",
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};
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if (span.name in nameMap) {
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// Unfortunately, the span name is not writable as this is not the intended behavior
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// but it is a workaround to set the correct span name
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(span as any).name = nameMap[span.name];
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}
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if ("ai.response.text" in attributes) {
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attributes[`${SpanAttributes.LLM_COMPLETIONS}.0.content`] =
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attributes["ai.response.text"];
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attributes[`${SpanAttributes.LLM_COMPLETIONS}.0.role`] = "assistant";
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delete attributes["ai.response.text"];
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}
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if ("ai.prompt.messages" in attributes) {
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try {
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const messages = JSON.parse(attributes["ai.prompt.messages"] as string);
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messages.forEach(
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(msg: { role: string; content: any }, index: number) => {
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attributes[`${SpanAttributes.LLM_PROMPTS}.${index}.content`] =
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typeof msg.content === "string"
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? msg.content
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: JSON.stringify(msg.content);
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attributes[`${SpanAttributes.LLM_PROMPTS}.${index}.role`] =
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msg.role;
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},
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);
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delete attributes["ai.prompt.messages"];
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} catch (e) {
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//Skip if JSON parsing fails
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}
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}
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if ("ai.usage.promptTokens" in attributes) {
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attributes[`${SpanAttributes.LLM_USAGE_PROMPT_TOKENS}`] =
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attributes["ai.usage.promptTokens"];
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delete attributes["ai.usage.promptTokens"];
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}
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if ("ai.usage.completionTokens" in attributes) {
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attributes[`${SpanAttributes.LLM_USAGE_COMPLETION_TOKENS}`] =
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attributes["ai.usage.completionTokens"];
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delete attributes["ai.usage.completionTokens"];
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}
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if (
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attributes[`${SpanAttributes.LLM_USAGE_PROMPT_TOKENS}`] &&
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attributes[`${SpanAttributes.LLM_USAGE_COMPLETION_TOKENS}`]
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) {
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attributes[`${SpanAttributes.LLM_USAGE_TOTAL_TOKENS}`] =
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Number(attributes[`${SpanAttributes.LLM_USAGE_PROMPT_TOKENS}`]) +
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Number(attributes[`${SpanAttributes.LLM_USAGE_COMPLETION_TOKENS}`]);
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}
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};
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// TODO: move this to the index.ts file
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// if (options.exporter) {
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// Telemetry.getInstance().capture("tracer:init", {
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// exporter: "custom",
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// processor: options.disableBatch ? "simple" : "batch",
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// });
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// } else {
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// Telemetry.getInstance().capture("tracer:init", {
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// exporter: options.baseUrl ?? "",
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// processor: options.disableBatch ? "simple" : "batch",
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// });
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// }
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return spanProcessor;
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}

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